Model Selection and Simplification Using Lattices
AbstractThis paper shows how to cope with a problem of model selection and simplification using the principle of coherence (Gabriel (1969): A procedure involving testing a set of models ought not accept a model while rejecting a more general model). The mathematical lattice theory is used to define a partial ordering over the space of considered models. Several examples of partial ordering in large families of models are given along with a searching algorithm to deter- mine the best model with respect to chosen criteria.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by EconWPA in its series Econometrics with number 0012004.
Length: 36 pages
Date of creation: 12 Feb 2001
Date of revision:
Note: Type of Document - Acrobat PDF; pages: 36 ; figures: included
Contact details of provider:
Web page: http://126.96.36.199
Model selection and simplification; Principle of coherence; Lattice of models; Regression; ARMA models;
Other versions of this item:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-02-27 (All new papers)
- NEP-ECM-2001-03-14 (Econometrics)
- NEP-EVO-2001-02-27 (Evolutionary Economics)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Bearse, Peter M & Bozdogan, Hamparsum & Schlottmann, Alan M, 1997. "Empirical Econometric Modelling of Food Consumption Using a New Informational Complexity Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(5), pages 563-86, Sept.-Oct.
- Damiani, Mirella & Panattoni, Lorenzo, 1992. "Optimal simulation with econometric models," Journal of Economic Dynamics and Control, Elsevier, vol. 16(1), pages 93-108, January.
- Dorfman, Jeffrey H. & Havenner, Arthur M., 1992. "A Bayesian approach to state space multivariate time series modeling," Journal of Econometrics, Elsevier, vol. 52(3), pages 315-346, June.
- Hendry, David F. & Learmer, Edward E. & Poirier, Dale J., 1990. "A Conversation on Econometric Methodology," Econometric Theory, Cambridge University Press, vol. 6(02), pages 171-261, June.
- repec:cup:etheor:v:6:y:1990:i:2:p:171-261 is not listed on IDEAS
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA).
If references are entirely missing, you can add them using this form.